Statistical Characterization and Prediction for a Stochastic Sea Environment

Designing marine and maritime systems requires the probabilistic characterization of sea waves in the time-history and spectral domains. These probabilistic models include parameters that can be empirically estimated based on limited data in durations, locations and applicability to particular designs. Characterizing the statistical uncertainties associated with the parameters and the models is an essential step for risk-based design methods. A framework is provided for characterizing and predicting the stochastic sea-state conditions using sampling and statistical methods in order to associate confidence levels with resulting estimates. Sea-state parameters are analyzed using statistical confidence intervals which give a clear insight for the uncertainties involved in the system. Hypothesis testing and goodness-of-fit are performed to demonstrate the statistical features. Moreover, sample size is required for performing statistical analysis. Sample size indicates the number of representative and independent observations. Current practices do not make a distinction between the number of discretization points for numerical computations and the number of sampling points, i.e. sample size needed for statistical analysis. Sample size and interval between samples to obtain independent observations are studied and compared with existing methods. Further, spatial relationship of the sea-state conditions describes the wave energy transferred through the wave movement. Locations of interest with unknown sea-state conditions are estimated using spatial interpolations. Spatial interpolation methods are proposed, discussed, and compared with the reported methods in the literature. This study will enhance the knowledge of sea-state conditions in a quantitative manner. The statistical feature of the proposed framework is essential for designing future marine and maritime systems using probabilistic modeling and risk analysis

Evaluating the Structural Effects of Property Tax Abatements on Economic Development Across Industries: Dissertation Summary

This research mainly comprises two empirical studies. First in an econometric analysis using statewide city-level data in Indiana, the first-difference model developed by Heckman and Hotz is applied to estimate the effect of property tax abatements (PTAs) over different sectors. The results indicate that a large majority of jobs created by the property tax abatement programs occur in the service sector, not the manufacturing sector. Despite the significant amount of attention focused on the manufacturing sector in discussions surrounding the implementation of property tax abatement programs, the analysis demonstrates that there is no significant contribution to employment in this sector. In addition, by applying the dummy variable technique, the analysis finds that the economic effect of PTAs diminishes over time. This finding confirms with the copycat behavior hypothesis proposed by previous scholars. Furthermore, the empirical results suggest that property tax abatements should be used only in the needy areas to maintain the long-term success of this program

Grey Situation Decision-making Algorithm to Optimize Silicon Wafer Slicing

The slicing of Silicon wafer is a complex manufacturing process in producing the raw materials for electronic chips and requires the efforts to effectively monitor the stability in production line and ensure the quality for the products composed of different shapes and materials. Human decision failure and other analytical errors are the most common source of management problems in such manufacturing stage. This paper presents a case regarding the silicon wafer manufacturing to examine the response to quality errors. The study has adopted the approach of grey situation decision-making algorithm for problem detection that suggests a technique to attain the quality control and reduce potential costs in production

Persistent currents in a graphene ring with armchair edges

A graphene nano-ribbon with armchair edges is known to have no edge state. However, if the nano-ribbon is in the quantum spin Hall (QSH) state, then there must be helical edge states. By folding a graphene ribbon to a ring and threading it by a magnetic flux, we study the persistent charge and spin currents in the tight-binding limit. It is found that, for a broad ribbon, the edge spin current approaches a finite value independent of the radius of the ring. For a narrow ribbon, inter-edge coupling between the edge states could open the Dirac gap and reduce the overall persistent currents. Furthermore, by enhancing the Rashba coupling, we find that the persistent spin current gradually reduces to zero at a critical value, beyond which the graphene is no longer a QSH insulator

Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domain-invariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed system. This allows one to generate diverse outputs from the learned latent distributions representing contents and styles. Moreover, to better capture the granularity of sound, such as the perceptual dimensions of timbre and the nuance in instrument-specific performance, cognitively plausible features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, are combined with the widely-used mel-spectrogram into a timber-enhanced multi-channel input representation. The Relativistic average Generative Adversarial Networks (RaGAN) is also utilized to achieve fast convergence and high stability. We conduct experiments on bilateral style transfer tasks among three different genres, namely piano solo, guitar solo, and string quartet. Results demonstrate the advantages of the proposed method in music style transfer with improved sound quality and in allowing users to manipulate the output